Introduction on “Self-supervised Difference Detection for Weakly-supervised Semantic Segmentation”

Abstract

In this paper, we introduce our ICCV (IEEE/CVF international conference on computer vision) 2019 paper¹⁾ that entitled “Self-supervised Difference Detection for Weakly-supervised Semantic Segmentation.” To minimize annotation costs associated with training of semantic segmentation models, weakly-supervised segmentation approaches have been studied. In recent weakly supervised segmentation methods, visualization-based approaches have been widely adopted. However, the visualization results are not generally equal to semantic segmentation. Therefore, to perform highly-accurate semantic segmentation, it is necessary to consider mapping functions that convert the visualization results into semantic segmentation. However, since such general mapping functions do not always guarantee improvement in accuracy. In the article, we consider that the results of mapping functions include noise and aim to improve accuracy by removing noise. To achieve that, in the article, we proposed self-supervised difference detection (SSDD) module which estimates noise from the results of mapping functions by predicting the difference between the segmentation masks.